Compared to the amount of research that has been done on English event extraction, there exists relatively little work on Chinese event extraction. We seek to push the frontiers of supervised Chinese event extraction research by proposing two extension to Li et al.'s (2012) state-of-the-art event extraction system. First, we employ a joint modeling approach to event extraction, aiming to address the error propagation problem inherent in Li et al.'s pipeline system architecture. Second, we investigate a variety of rich knowledge sources for Chinese event extraction that encode knowledge ranging from the character level to the discourse level. Experimental results on the ACE 2005 dataset show that our joint-modeling, knowledge-rich approach significantly outperforms Li et al.'s approach. © 2012 The COLING.
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